Understanding the behavior of your customers is key to improving and maintaining revenue streams. It is a an important part when crafting successful marketing campaigns. With SAS Visual Analytics 7.1 you can analyze, explore and visualize user behavior, click paths and other event-based scenarios. Monitoring the customer journey by visualizing all touch-points in your organisation will help you to identify gaps and improve the overall customer experience. Flow visualizations will help you to best understand hotspots, highlight common trends and find insights in individual user or aggregated paths.
In path analysis you are typically trying to determine a sequence of events in a particular time window. For example you pay attention to paths more frequently used than others in order to understand what path prospects take before they become new customers. Path analysis works best with linear event streams such as customer life cycle (1. prospect, 2. trial subscription, 3. customer, 4. product upgrade, etc.) but is also commonly used for web usage analysis. As a data scientist you may look for optimal paths to compare with paths customers have actual taken. This often reveals interesting insight and opportunities for revenue improvements.
Path analysis can be challenging especially when analyzing web usage. There are often many ways customers can navigate on a website, so even if we determine the optimal path in this scenario, it’s very likely that just a very minor number of users will actually take the optimal path. This means you must pay special attention to the path analysis results in order to gain the right insights. You may for example compare the least and most used paths in terms of sequence count or the number of drop off’s (e.g. customers who left a session and therefore didn't complete an order).
It can be useful to apply segmentation to path analysis (more details below), as this will greatly reduce the number of steps in a path and may represent a better aggregated view about paths taken. In most cases, you are after the number of people taking the optimal path to reach your goal such as purchasing a product. Once you are understanding common paths you can try to influence the customer behavior by redesigning the web page or starting a marketing campaign, for example.
Path analysis in SAS Visual Analytics
Let’s start with a very basic example about path analysis to explain the basic steps. Consider the following simple data set:
The table structure shows our customers (John, Jane and Bob) and the visited web pages (item column) per session (transId column). As you can see the customer “John” visited our web page twice at different times. The sequence column is just used to maintain the order of the events. Typically you would take a date/timestamp here.
Since this is very simple data set you can easily see what paths each customer has taken:
- John: ABC, ADE
- Jane: BDEED
- Bob: AFD
Visualizing this example in SAS Visual Analytics provides the following Sankey diagram:
The diagram is colored by path indicating that there are 5x different paths including a drop off (path 2, red). This already gives interesting insights such as partly shared paths (John/green and Jane/turquoise share event D & E) as well as a common start event (A).
As part of the path analysis in SAS Visual Analytics you can also change the link aggregation and colorization. Switching the aggregation to color links by event shows the following:
Again highlighting the common partial path in yellow. By default the diagram uses the sequence count or frequency as default link width. However, you may want to weigh paths by a given measure, such as purchases or revenue as this better reflects the impact a path may have. The following example shows a currency measure assigned as path weight:
Let’s look at a more advanced data set with a few more events to analyze. Note, that this data set is just a small extract of a real web site access log file. You will see how quickly the number of paths increases and things like ranking and segmentation will play an important role:
Similar to the first data set we are looking at customers visited specific pages on our website over a period of time. The increased number of potential pages or events also mean an increased number of potential paths a customer can take. Let’s look at a first visualization of this data source:
Not surprisingly most customers enter our web page via the welcome page. This could be mainly driven by the fact that users typically click on the first link in search engines rather than one of the sub categories. As you can see the paths taken are very long making this diagram very wide – tools such as the overview panel or path selection help navigating in the diagram:
Segmentation for path analysis
One of the methods to reduce the overall number of events is to group events. SAS Visual Analytics provides methods to create custom categories. In our example we are going to group a number of events into groups such Buy, Search and Product:
Applying this new custom group item to the Sankey diagram provides an aggregated and simplified view of paths taken:
Once you have determined a particular path of interest you often want to further analyze the related group of customers having taken this path. For instance to include the group of individuals in your next marketing campaign. SAS Visual Analytics allows you to narrow down the selection by either filtering or merging into a new visualization.
Path filtering is done by selecting one or more events and either include or exclude the items by various conditions:
In this example we are only interested in paths starting with the welcome page:
Note, that the user can go ahead use the current filtered selection to create new visualization for further analysis.
Given the high number of potential paths a custom can take you may also just concentrate on the top or bottom ranked paths. SAS Visual Analytics provides a number of options to filter and rank paths shown in the following property panel:
Given the new top 5 ranking settings and the selected vertical layout the diagram renders as follow giving you great understanding of the flow users take in the 5 most common paths.
SAS Visual Analytics provides a robust platform for analytic discovery on your data. Path analysis is important when trying to understand your customers' behavior online. From basic web usage to campaign and attribute analysis, gaining insights from your data will help drive your next email campaign or paid search or banner ad. Even in customer life cycle monitoring as part of acquisition and retention analysis you can quickly see how customer touch points such as email offers, call center sessions or branch visits pay out.